rpbooks Chapter 72 Verification
Chapter 72 Verification
In early November 2019.
Central Java, Indonesia.
AgriNusa's palm plantation is located 70 kilometers from Semarang city. The 3,200-acre plantation stretches out like a deep green sea under the tropical sun. The palm trees stand straight, their canopies intertwined, resembling a roughly cut emerald mosaic when viewed from above.
Xiao Chen was squatting on an open space, adjusting flight control parameters. His T-shirt was soaked with sweat, and his back was covered in a sticky layer of moisture. Next to him, Liu Yu was staring at the real-time data stream on his laptop, occasionally raising his hand to wipe his forehead.
Two inspection drones equipped with Hongyuan's industrial-grade flight control modules were parked on a moisture-proof mat. The fuselage is larger than the F4, with thicker arms, and a high-resolution multispectral camera is mounted on the bottom. This is AgriNusa's own airframe—Hongyuan only provides the flight control and core sensor modules.
"GPS signal is stable, RTK base station is calibrated." Xiao Chen stood up and handed the remote control to the AgriNusa pilot.
The first test: the accuracy of RTK centimeter-level positioning in a palm garden environment.
The pilot pushes the stick to take off. After the drone ascends to an altitude of twenty meters, it begins to fly along the preset waypoint. The canopy of a palm tree is between ten and fifteen meters high, and the drone needs to maintain precise position control within a space of five to eight meters above the canopy.
刘羽盯着屏幕上的轨迹偏差数据。数字在跳动——3厘米、5厘米、7厘米、4厘米。偶尔跳到9厘米，但很快回落。
"The average deviation is 6.2 centimeters, and the maximum deviation is 9.1 centimeters," Liu Yu read out the data. "Within 10 centimeters. Meets the standard."
Adi, the CTO of AgriNusa, stood to the side, taking notes in a notebook. He nodded but didn't say anything.
The second test: performance of binocular visual obstacle avoidance among dense palm trees.
This is the real challenge. The palm grove is not flat farmland; the spacing between the tree trunks is irregular, and the lighting conditions are constantly changing—sunlight filters through the canopy, creating dappled shadows and extremely intense alternations between light and dark.
The pilot lowered the drone to a height of eight meters and began an S-shaped flight test between the palm trees.
The first five minutes were perfect. The drone identified each tree trunk and automatically adjusted its course to avoid it. The turns were crisp and clean, without any hesitation.
The problem occurred in the sixth minute.
A cloud drifted by, and the sunlight suddenly shifted from strong to weak and then back to strong. The light underwent a dramatic change in brightness in less than two seconds. In that instant, the drone suddenly hovered—it had misjudged the shadow behind a tree trunk as an obstacle and triggered an emergency avoidance maneuver.
Although it didn't hit anything, this misjudgment caused the drone to deviate from its planned flight path by about two meters.
Liu Yu immediately recorded the environmental parameters in his notebook: the light intensity dropped from 42000 lux to 8000 lux in 1.8 seconds and then rose back to 38000 lux. The exposure compensation of the binocular vision could not keep up with this rate of change.
The test continued. A similar misjudgment occurred again in the next fifteen minutes, also during a moment of drastic change in lighting.
"Both misjudgments were related to sudden changes in lighting," Liu Yu said, closing his notebook. "Other times, obstacle avoidance was perfectly normal. This is a solvable problem—the key lies in the response speed of the exposure compensation algorithm."
Adi listened from the side, his expression changing from serious to thoughtful.
The third test: coverage of automated route planning in irregular plantations.
This test took the longest. The drones needed to automatically generate inspection routes based on a pre-imported park map, covering all planting areas.
Forty minutes later, the results came back. Coverage was 92 percent. The remaining 8 percent was concentrated in the northwest corner of the park—the terrain data for that area was not accurate enough, preventing the flight planner from generating a reliable flight path.
"This can be resolved by supplementing the terrain data," Xiao Chen said. "It's not a problem with the flight control system itself."
All three tests have been completed.
As Xiao Chen and Liu Yu were packing up their equipment, Adi walked over. He had been standing in the tropical sun all morning, and the sweat on his shirt had dried and then soaked three or four times.
"Mr. Chen, Mr. Liu," Adi said in heavily accented English, "this is the best inspection flight control solution we've ever tested."
He paused for a moment, then added, "If the obstacle avoidance lighting issue is resolved, we are willing to sign a long-term procurement agreement. Not one or two years—we're talking about three to five years."
Liu Yu and Xiao Chen exchanged a glance.
That evening, Xiao Chen sent the complete test report back to Shenzhen. It included all the data, videos, environmental parameters, and Adi's exact words.
Su Chen received the report at 11 p.m. He reviewed the test data twice and then opened the virtual disassembly lab.
In the lab, he deployed the complete algorithm architecture of the binocular vision obstacle avoidance module. He understood the root cause of the misjudgment the moment he saw the data—the exposure compensation response time was 200 milliseconds, but in tropical bright light conditions, changes in illumination caused by cloud cover could occur within 100 milliseconds. The algorithm couldn't keep up with the speed of the real world.
There are two directions for solutions. One is to improve the response speed of the image sensor at the hardware level, but this means replacing the sensor, which is both costly and time-consuming. The other is to implement predictive compensation at the software level—adjusting exposure parameters in advance by analyzing trends in lighting conditions, rather than reacting passively.
Su Chen chose the second path. He spent three hours simulating the lighting conditions of a tropical palm plantation in a virtual environment, testing four different prediction and compensation algorithms, and finally selected an adaptive scheme based on a sliding window. This scheme can reduce the response time to less than 80 milliseconds.
At 2 a.m., he sent the optimization plan to Liu Yu.
A week later, Liu Yu verified the results in the laboratory. The measured compensation response period was 72 milliseconds, and there were no misjudgments under simulated tropical light abrupt changes.
Indonesian scenario – passed.
The test in Shaoguan is scheduled for March. That scenario will be even more challenging. But Su Chen isn't in a hurry. He knows the level of skill he already possesses in flight control.
The next step is to prove that it can adapt to more extreme environments.